Method of generating a copy detection pattern (CDP), or a portion of a CDP, for printing on a substrate, comprising: generating a plurality of digital files, each of an image comprising an at least partially random two dimensional (2D) distribution of dark and light pixels, and applying an optimization process configured to increase a copy detection performance of a resultant digital file output by the method, said optimization process comprising a) comparing a copy detection performance of a first of said plurality of digital files, which constitutes a test digital file, with a copy detection performance of another of said plurality of digital files modified with respect to said test digital file, which constitutes a modified digital file, b) replacing the test digital file with said modified digital file, if the copy detection performance of said modified digital file is greater than the copy detection performance of the test digital file, in which case the modified digital file becomes the test digital file, and c) repeating steps a) and b) until a termination condition is met.

A method includes: capturing an image; partitioning the image into sub-images; for each sub-image: providing the sub-image to a detection model, receiving, from the detection model, one or more sub-image regions of interest (SROIs), each SROI defined by (i) a position of the SROI, and (ii) one of a set of symbology categories, each symbology category encompassing a plurality of barcode symbologies; generating one or more regions of interest (ROI) from the SROIs, each ROI defined by a position of the ROI in the image, and a symbology category; and providing the ROIs to a decoder.

Methods, apparatuses and computer program products for providing artificial-intelligence-based indicia data editing are provided. For example, an example computer-implemented method may include determining, based at least in part on a data processing model associated with a scan setting module, a first decoded data string corresponding to a first indicia; determining, based at least in part on user input data, a first input data string corresponding to the first indicia; generating a predictive indicia data editing model based at least in part on providing the first decoded data string and the first input data string to an artificial intelligence algorithm; and updating the scan setting module based at least in part on the predictive indicia data editing model.

Methods, apparatuses and computer program products for providing artificial-intelligence-based indicia data editing are provided. For example, an example computer-implemented method may include determining, based at least in part on a data processing model associated with a scan setting module, a first decoded data string corresponding to a first indicia; determining, based at least in part on user input data, a first input data string corresponding to the first indicia; generating a predictive indicia data editing model based at least in part on providing the first decoded data string and the first input data string to an artificial intelligence algorithm; and updating the scan setting module based at least in part on the predictive indicia data editing model.

The disclosure includes a fixed retail scanner including a data reader, comprising a main board including one or more processors including a system processor, one or more camera modules, and an artificial intelligence (AI). The system processor is configured to transmit image data received from the one or more camera modules responsive to one or more event triggers detected by the system processor, and wherein the AI accelerator is configured to perform analysis based on an AI engine local to the AI accelerator in response to the event trigger. A remote server may also be operably coupled to the fixed retail scanner through the multi-port network switch, the remote server having a remote AI engine stored therein, wherein the local AI engine within the fixed retail scanner is a simplified AI model relative to the remote AI engine within the remote server.

The invention relates to a method for decoding a machine readable code, comprising the machine generation of an input image of the machine readable code, wherein the input image has a plurality of image pixels having a respective value, wherein the sequence of the values of the image pixels defines an input signal development, wherein the input signal development comprises signal regions that each represent the signal energy in an associated signal region. The method comprises the transfer of the signal energy of at least one first signal region to a second signal region, wherein a result signal development is produced by transferring the signal energy. Finally, the method comprises the decoding of the machine readable code based on the result signal development.

Product scanner based radar systems are provided herein. An example product scanner includes a housing, an indicia scanner, and a radar system further comprising a radar chip and an antenna. In the example, the indicia scanner is configured to capture indicia data from a product indicia disposed within an indicia scan region defined by an optical field-of-view of the optical sensor. In the example, the radar system is configured to capture first three-dimensional layer data representative of an exterior feature of a product disposed within a radar field-of-view. In the example, the radar system is configured to capture second three-dimensional layer data representative of an interior feature of the product disposed within the radar field-of-view.

The present disclosure relates to a process for identifying a binary dot matrix code on a mold surface, comprising: capturing a plurality of binary dot matrix code images of the mold surface; inputting the acquisition data into the YOLOv8 network model for training; recapturing the images to be tested and inputting them into the upgraded YOLOv8 network model for detection and output; calculating positional coordinates of vertices at the outermost periphery of the four bounding boxes based on the output results to obtain the distorted quadrilateral, which is converted into the square pattern by the perspective transformation; inputting the square pattern into the upgraded YOLOv8 network model for detection and outputting the results, and calculating the counterclockwise rotation angle ; obtaining the standard pattern by rotating the square pattern by the angle ; dividing the standard pattern into the image blocks with the same size in equal portions, and identifying the circular code points of the image blocks, concatenating the identification results to obtain the binary-encoded sequence data; and decoding the binary-encoded sequence data based on binary-encoded decoding rules, and deriving corresponding binary-encoded character information.